Project Summary: Rapid sequencing methods identified functional RNA sequences across all domains of life. Additionally, structure mapping methods demonstrate extensive in vivo structure for RNA, including messenger RNAs. Determining the roles of RNA structures and their mechanisms of action is central to biology and human health. RNA secondary structure prediction is one of the tools that is commonly used to aid in understanding RNA function, and we addressed the need for RNA secondary structure prediction by developing the software package RNAstructure. RNAstructure is a user-friendly software package for RNA secondary structure prediction, display, and analysis. It includes methods for structure prediction of a single sequence, including pseudoknots, structure prediction for bimolecular interactions, and prediction of the conserved structure for multiple homologous sequences. It can use structure mapping data, including mapping with chemical agents and enzymes that reveal unpaired nucleotides, to improve the accuracy of structure prediction. It can also predict unpaired regions in RNA, and these predictions are essential for siRNA and antisense oligonucleotide design. Thermodynamic parameters are provided for both RNA and DNA sequences, which extends the structure predictions to DNA. The programs are available with a graphical user interface (for Windows, Mac OS X, or Linux), command line interfaces, and also as web servers. The algorithms are also available for use in other programs as a set of well-documented C++ classes. The package is fully open source, under the GNU Public License. For the next period of support, we propose high-impact aims that will keep RNAstructure cutting-edge in its ability to make new types of structure predictions needed by the community. We will update the nearest neighbor parameters using the latest experimental results, and expand the parameters to include nucleotides beyond A, C, G, and U that result from post-transcriptional modification. We will also expand our algorithms to improve the accuracy of pseudoknot prediction. Finally, we will continue to support our community of users and developers.